MQL4 and MQL5 Programming Articles

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Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.

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Pattern Recognition Using Dynamic Time Warping in MQL5

Pattern Recognition Using Dynamic Time Warping in MQL5

In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5.
Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)
Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)

Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)

We continue to complement the Rendered table (CCanvasTable) with new features. The table will now have: highlighting of the rows when hovered; ability to add an array of icons for each cell and a method for switching them; ability to set or modify the cell text during the runtime, and more.
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Price Action Analysis Toolkit Development (Part 57): Developing a Market State Classification Module in MQL5

Price Action Analysis Toolkit Development (Part 57): Developing a Market State Classification Module in MQL5

This article develops a market state classification module for MQL5 that interprets price behavior using completed price data. By examining volatility contraction, expansion, and structural consistency, the tool classifies market conditions as compression, transition, expansion, or trend, providing a clear contextual framework for price action analysis.
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Timeseries in DoEasy library (part 57): Indicator buffer data object

Timeseries in DoEasy library (part 57): Indicator buffer data object

In the article, develop an object which will contain all data of one buffer for one indicator. Such objects will be necessary for storing serial data of indicator buffers. With their help, it will be possible to sort and compare buffer data of any indicators, as well as other similar data with each other.
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Category Theory in MQL5 (Part 15) : Functors with Graphs

Category Theory in MQL5 (Part 15) : Functors with Graphs

This article on Category Theory implementation in MQL5, continues the series by looking at Functors but this time as a bridge between Graphs and a set. We revisit calendar data, and despite its limitations in Strategy Tester use, make the case using functors in forecasting volatility with the help of correlation.
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Reimagining Classic Strategies in Python: MA Crossovers

Reimagining Classic Strategies in Python: MA Crossovers

In this article, we revisit the classic moving average crossover strategy to assess its current effectiveness. Given the amount of time since its inception, we explore the potential enhancements that AI can bring to this traditional trading strategy. By incorporating AI techniques, we aim to leverage advanced predictive capabilities to potentially optimize trade entry and exit points, adapt to varying market conditions, and enhance overall performance compared to conventional approaches.
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Developing a trading Expert Advisor from scratch (Part 25): Providing system robustness (II)

Developing a trading Expert Advisor from scratch (Part 25): Providing system robustness (II)

In this article, we will make the final step towards the EA's performance. So, be prepared for a long read. To make our Expert Advisor reliable, we will first remove everything from the code that is not part of the trading system.
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Population optimization algorithms: Artificial Bee Colony (ABC)

Population optimization algorithms: Artificial Bee Colony (ABC)

In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.
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Using PSAR, Heiken Ashi, and Deep Learning Together for Trading

Using PSAR, Heiken Ashi, and Deep Learning Together for Trading

This project explores the fusion of deep learning and technical analysis to test trading strategies in forex. A Python script is used for rapid experimentation, employing an ONNX model alongside traditional indicators like PSAR, SMA, and RSI to predict EUR/USD movements. A MetaTrader 5 script then brings this strategy into a live environment, using historical data and technical analysis to make informed trading decisions. The backtesting results indicate a cautious yet consistent approach, with a focus on risk management and steady growth rather than aggressive profit-seeking.
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Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage made easier

Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage made easier

When working with machine learning models, it’s essential to ensure consistency in the data used for training, validation, and testing. In this article, we will create our own version of the Pandas library in MQL5 to ensure a unified approach for handling machine learning data, for ensuring the same data is applied inside and outside MQL5, where most of the training occurs.
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Matrix Utils, Extending the Matrices and Vector Standard Library Functionality

Matrix Utils, Extending the Matrices and Vector Standard Library Functionality

Matrix serves as the foundation of machine learning algorithms and computers in general because of their ability to effectively handle large mathematical operations, The Standard library has everything one needs but let's see how we can extend it by introducing several functions in the utils file, that are not yet available in the library
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Moving to MQL5 Algo Forge (Part 2): Working with Multiple Repositories

Moving to MQL5 Algo Forge (Part 2): Working with Multiple Repositories

In this article, we are considering one of the possible approaches to organizing the storage of the project's source code in a public repository. We will distribute the code across different branches to establish clear and convenient rules for the project development.
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DoEasy. Controls (Part 29): ScrollBar auxiliary control

DoEasy. Controls (Part 29): ScrollBar auxiliary control

In this article, I will start developing the ScrollBar auxiliary control element and its derivative objects — vertical and horizontal scrollbars. A scrollbar is used to scroll the content of the form if it goes beyond the container. Scrollbars are usually located at the bottom and to the right of the form. The horizontal one at the bottom scrolls content left and right, while the vertical one scrolls up and down.
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Risk Management (Part 5): Integrating the Risk Management System into an Expert Advisor

Risk Management (Part 5): Integrating the Risk Management System into an Expert Advisor

In this article, we will implement the risk management system developed in previous publications and add the Order Blocks indicator described in other articles. In addition, we will run a backtest so we can compare results with the risk management system enabled and evaluate the impact of dynamic risk.
Interview with Egidijus Bockus (ATC 2012)
Interview with Egidijus Bockus (ATC 2012)

Interview with Egidijus Bockus (ATC 2012)

"I examined many indicators before realizing that they are not necessary for making money on Forex" - our present interviewee Egidijus Bockus (Egidijus) told us boldly. We have all reasons to take his words seriously, as his Expert Advisor occupies the third place with more than $32 000 beginning from the third week of the Automated Trading Championship 2012.
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Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)

Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)

We will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.
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Developing a multi-currency Expert Advisor (Part 5): Variable position sizes

Developing a multi-currency Expert Advisor (Part 5): Variable position sizes

In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.
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Understand and Efficiently use OpenCL API by Recreating built-in support as DLL on Linux (Part 2): OpenCL Simple DLL implementation

Understand and Efficiently use OpenCL API by Recreating built-in support as DLL on Linux (Part 2): OpenCL Simple DLL implementation

Continued from the part 1 in the series, now we proceed to implement as a simple DLL then test with MetaTrader 5. This will prepare us well before developing a full-fledge OpenCL as DLL support in the following part to come.
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Multiple Symbol Analysis With Python And MQL5 (Part 3): Triangular Exchange Rates

Multiple Symbol Analysis With Python And MQL5 (Part 3): Triangular Exchange Rates

Traders often face drawdowns from false signals, while waiting for confirmation can lead to missed opportunities. This article introduces a triangular trading strategy using Silver’s pricing in Dollars (XAGUSD) and Euros (XAGEUR), along with the EURUSD exchange rate, to filter out noise. By leveraging cross-market relationships, traders can uncover hidden sentiment and refine their entries in real time.
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Practicing the development of trading strategies

Practicing the development of trading strategies

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.
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From Novice to Expert: Parameter Control Utility

From Novice to Expert: Parameter Control Utility

Imagine transforming the traditional EA or indicator input properties into a real-time, on-chart control interface. This discussion builds upon our foundational work in the Market Periods Synchronizer indicator, marking a significant evolution in how we visualize and manage higher-timeframe (HTF) market structures. Here, we turn that concept into a fully interactive utility—a dashboard that brings dynamic control and enhanced multi-period price action visualization directly onto the chart. Join us as we explore how this innovation reshapes the way traders interact with their tools.
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Utilizing CatBoost Machine Learning model as a Filter for Trend-Following Strategies

Utilizing CatBoost Machine Learning model as a Filter for Trend-Following Strategies

CatBoost is a powerful tree-based machine learning model that specializes in decision-making based on stationary features. Other tree-based models like XGBoost and Random Forest share similar traits in terms of their robustness, ability to handle complex patterns, and interpretability. These models have a wide range of uses, from feature analysis to risk management. In this article, we're going to walk through the procedure of utilizing a trained CatBoost model as a filter for a classic moving average cross trend-following strategy.
Andrey Voitenko: Programming errors cost me $15,000 (ATC 2010)
Andrey Voitenko: Programming errors cost me $15,000 (ATC 2010)

Andrey Voitenko: Programming errors cost me $15,000 (ATC 2010)

Andrey Voitenko is participating in the Automated Trading Championship for the first time, but his Expert Advisor is showing mature trading. For already several weeks Andrey's Expert Advisors has been listed in the top ten and seems to be continuing his positive performance. In this interview Andrey is telling about his EA's features, errors and the price they cost him.
Two-Stage Modification of Opened Positions
Two-Stage Modification of Opened Positions

Two-Stage Modification of Opened Positions

The two-stage approach allows you to avoid the unnecessary closing and re-opening of positions in situations close to the trend and in cases of possible occurrence of divirgence.
Testing Visualization: Account State Charts
Testing Visualization: Account State Charts

Testing Visualization: Account State Charts

Enjoy the process of testing with charts, displaying the balance - now all the necessary information is always in view!
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Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D)

Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D)

We invite you to get acquainted with a new approach to detecting objects using hypernetworks. A hypernetwork generates weights for the main model, which allows taking into account the specifics of the current market situation. This approach allows us to improve forecasting accuracy by adapting the model to different trading conditions.
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From Novice to Expert: Animated News Headline Using MQL5 (VI) — Pending Order Strategy for News Trading

From Novice to Expert: Animated News Headline Using MQL5 (VI) — Pending Order Strategy for News Trading

In this article, we shift focus toward integrating news-driven order execution logic—enabling the EA to act, not just inform. Join us as we explore how to implement automated trade execution in MQL5 and extend the News Headline EA into a fully responsive trading system. Expert Advisors offer significant advantages for algorithmic developers thanks to the wide range of features they support. So far, we’ve focused on building a news and calendar events presentation tool, complete with integrated AI insights lanes and technical indicator insights.
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Introduction to MQL5 (Part 42): Beginner Guide to File Handling in MQL5 (IV)

Introduction to MQL5 (Part 42): Beginner Guide to File Handling in MQL5 (IV)

This article shows how to build an MQL5 indicator that reads a CSV trading history, extracts Profit($) values and total trades, and computes a cumulative balance progression. We plot the curve in a separate indicator window, auto-scale the Y-axis, and draw horizontal and vertical axes for alignment. The indicator updates on a timer and redraws only when new trades appear. Optional labels display per-trade profit and loss to help assess performance and drawdowns directly on the chart.
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Neural networks made easy (Part 35): Intrinsic Curiosity Module

Neural networks made easy (Part 35): Intrinsic Curiosity Module

We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.
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Python-MetaTrader 5 Strategy Tester (Part 04): Tester 101

Python-MetaTrader 5 Strategy Tester (Part 04): Tester 101

In this fascinating article, we build our very first trading robot in the simulator and run a strategy testing action that resembles how the MetaTrader 5 strategy tester works, then compare the outcome produced in a custom simulation against our favorite terminal.
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Integrating ML models with the Strategy Tester (Part 3): Managing CSV files (II)

Integrating ML models with the Strategy Tester (Part 3): Managing CSV files (II)

This material provides a complete guide to creating a class in MQL5 for efficient management of CSV files. We will see the implementation of methods for opening, writing, reading, and transforming data. We will also consider how to use them to store and access information. In addition, we will discuss the limitations and the most important aspects of using such a class. This article ca be a valuable resource for those who want to learn how to process CSV files in MQL5.
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Category Theory in MQL5 (Part 3)

Category Theory in MQL5 (Part 3)

Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL5 community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that provides insight while hopefully furthering the use of this remarkable field in Traders' strategy development.
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Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts

Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts

This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.
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MQL5 Wizard Techniques you should know (Part 84): Using Patterns of Stochastic Oscillator and the FrAMA - Conclusion

MQL5 Wizard Techniques you should know (Part 84): Using Patterns of Stochastic Oscillator and the FrAMA - Conclusion

The Stochastic Oscillator and the Fractal Adaptive Moving Average are an indicator pairing that could be used for their ability to compliment each other within an MQL5 Expert Advisor. We introduced this pairing in the last article, and now look to wrap up by considering its 5 last signal patterns. In exploring this, as always, we use the MQL5 wizard to build and test out their potential.
Interview with Alexander Prishchenko (ATC 2012)
Interview with Alexander Prishchenko (ATC 2012)

Interview with Alexander Prishchenko (ATC 2012)

What can be more complicated than a multicurrency trading robot? Surely, it is an automated strategy based on Elliott Wave Principle. Can we imagine something more complicated than that? Yes, we can. It is a multicurrency Expert Advisor using Elliott Waves on each currency pair! Alexander Prishchenko (Crucian) believes that even a newcomer can learn the rules.
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Algorithmic Trading Strategies: AI and Its Road to Golden Pinnacles

Algorithmic Trading Strategies: AI and Its Road to Golden Pinnacles

This article demonstrates an approach to creating trading strategies for gold using machine learning. Considering the proposed approach to the analysis and forecasting of time series from different angles, it is possible to determine its advantages and disadvantages in comparison with other ways of creating trading systems which are based solely on the analysis and forecasting of financial time series.
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How to Detect Round-Number Liquidity in MQL5

How to Detect Round-Number Liquidity in MQL5

The article presents an MQL5 method for detecting psychological round numbers by converting prices to strings and counting trailing zeros (ZeroSize). It outlines the theory of institutional liquidity at integers, explains the GetZeroCount logic with tick-size normalization to avoid floating‑point errors, and details hierarchical visualization. Case studies across forex, metals, and crypto, plus timeframe filters and inputs, show how to use confluence and basic risk controls in practice.
Market Diagnostics by Pulse
Market Diagnostics by Pulse

Market Diagnostics by Pulse

In the article, an attempt is made to visualize the intensity of specific markets and of their time segments, to detect their regularities and behavior patterns.
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SQLite capabilities in MQL5: Example of a dashboard with trading statistics by symbols and magic numbers

SQLite capabilities in MQL5: Example of a dashboard with trading statistics by symbols and magic numbers

In this article, we will consider creating an indicator that displays trading statistics on a dashboard by account and by symbols and trading strategies. We will implement the code based on examples from the Documentation and the article on working with databases.
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Pipelines in MQL5

Pipelines in MQL5

In this piece, we look at a key data preparation step for machine learning that is gaining rapid significance. Data Preprocessing Pipelines. These in essence are a streamlined sequence of data transformation steps that prepare raw data before it is fed to a model. As uninteresting as this may initially seem to the uninducted, this ‘data standardization’ not only saves on training time and execution costs, but it goes a long way in ensuring better generalization. In this article we are focusing on some SCIKIT-LEARN preprocessing functions, and while we are not exploiting the MQL5 Wizard, we will return to it in coming articles.